This model is a fine-tuned version of the Qwen2.5-3B base model, enhanced using Low-Rank Adaptation (LoRA) techniques via the MLX framework. The fine-tuning process utilized the isaiahbjork/chain-of-thought dataset, comprising 7,143 examples, over 600 iterations. This enhancement aims to improve the model's performance in tasks requiring multi-step reasoning and problem-solving.
Model Architecture
Base Model: Qwen2.5-3B
Model Type: Causal Language Model
Architecture: Transformer with Rotary Position Embedding (RoPE),
SwiGLU activation, RMSNorm normalization, attention QKV bias, and tied word embeddings
Parameters: 3.09 billion
Layers: 36
Attention Heads: 16 for query, 2 for key and value (GQA)
Fine-Tuning Details
Technique: Low-Rank Adaptation (LoRA)
Framework: MLX
Dataset: isaiahbjork/chain-of-thought
Dataset Size: 7,143 examples
Iterations: 600
LoRA was employed to efficiently fine-tune the model by adjusting a subset of parameters, reducing computational requirements while maintaining performance. The MLX framework facilitated this process, leveraging Apple silicon hardware for optimized training.
